New tools for evaluating LQAS survey designs

Emerging Themes in Epidemiology

Lauren Hund¹ 

[1] Department of Family and Community Medicine, University of New Mexico, 2400 Tucker Avenue Northeast, Albuquerque NM 87131, USA

关键词: Survey design;    LQAS;    Acceptance sampling;   

Others  :  800915
DOI  :  10.1186/1742-7622-11-2

 received in 2013-06-01, accepted in 2014-01-21,  发布年份 2014

【 摘 要 】

Lot Quality Assurance Sampling (LQAS) surveys have become increasingly popular in global health care applications. Incorporating Bayesian ideas into LQAS survey design, such as using reasonable prior beliefs about the distribution of an indicator, can improve the selection of design parameters and decision rules. In this paper, a joint frequentist and Bayesian framework is proposed for evaluating LQAS classification accuracy and informing survey design parameters. Simple software tools are provided for calculating the positive and negative predictive value of a design with respect to an underlying coverage distribution and the selected design parameters. These tools are illustrated using a data example from two consecutive LQAS surveys measuring Oral Rehydration Solution (ORS) preparation. Using the survey tools, the dependence of classification accuracy on benchmark selection and the width of the ‘grey region’ are clarified in the context of ORS preparation across seven supervision areas. Following the completion of an LQAS survey, estimation of the distribution of coverage across areas facilitates quantifying classification accuracy and can help guide intervention decisions.

【 授权许可】

   
2014 Hund; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]Dodge H, Romig H: Sampling inspection tables: single and double sampling. New York: Wiley; 1959.
• [2]Hald A: Statistical theory of sampling inspection by attributes. New York: Academic Press; 1981.
• [3]Robertson S, Valadez J: Global review of health care surveys using lot quality assurance sampling (LQAS), 1984-2004. Soc Sci Med 2006, 63(6):1648-1660.
• [4]Rhoda D, Fernandez S, Fitch D, Lemeshow S: LQAS: user beware. Int J Epidemiol 2010, 39(1):60-68.
• [5]Bilukha O, Blanton C: Interpreting results of cluster surveys in emergency settings: is the LQAS test the best option. Emerg Themes Epidemiol 2008, 5:25. BioMed Central Full Text
• [6]Sandiford P: Lot quality assurance sampling for monitoring immunization programmes: cost-efficient or quick and dirty? Health Policy Plann 1993, 8(3):217-223.
• [7]Bilukha O: Old and new cluster designs in emergency field surveys: in search of a one-fits-all solution. Emerg Themes Epidemiol 2008., 5(7) doi:10.1186/1742-7622-5-7
• [8]Olives C, Pagano M: Bayes-LQAS: classifying the prevalence of global acute malnutrition. Emerg Themes Epidemiol 2010, 7:3. BioMed Central Full Text
• [9]Olives C, Pagano M: Choosing a design to fit the situation: how to improve specificity and positive predictive values using Bayesian lot quality assurance sampling. Int J Epidemiol 2013, 42(1):346-355.
• [10]Myatt M, Bennett D: A novel sequential sampling technique for the surveillance of transmitted HIV drug resistance by cross-sectional survey for use in low resource settings. Antivir Ther Suppl 2008, 13(2):37-48.
• [11]Greenland S, Poole C: Living with P Values: resurrecting a Bayesian Perspective on Frequentist Statistics. Epidemiology 2013, 24(1):62-68.
• [12]Senn S: You may believe you are a Bayesian but you are probably wrong. Rationality Markets Morals 2011, 2(42):48-66.
• [13]Valadez J, Devkota B: Decentralized supervision of community health program using LQAS in two districts of southern Nepal. In Community-Based Health Care: Lessons Bangladesh to Boston. Edited by Rohde JE, Wyon JB. Boston: Manag Sci Health in collaboration with the Harvard School of Public Health; 2002:169-200.
• [14]Gelman A: Commentary: P values and statistical practice. Epidemiology 2013, 24(1):69-72.
• [15]R Core Team: R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2012. [ISBN 3-900051-07-0, http://www.R-project.org/ webcite]
• [16]RStudio Inc: shiny: Web Application, Framework for R, R package. 2013. [Version 0.5.0]. http://CRAN.R-project.org/package=shiny webcite
• [17]Scott D: Multivariate density estimation: theory, practice, and visualization. New York: Wiley; 1992.
• [18]Turlach B: Bandwidth selection in kernel density estimation: a review. CORE Institut de Statistique vol 2, (Discussion paper 9317), Institut de Statistique, Universite Catholique de Louvain, 1993
• [19]Izenman A: Review papers: recent developments in nonparametric density estimation. J Am Stat Assoc 1991, 86(413):205-224.
• [20]Silverman B: Density estimation for statistics and data analysis. London: Chapman & Hall; 1986.
• [21]Wand M, Marron J, Ruppert D: Transformations in density estimation. J Am Stat Assoc 1991, 86(414):343-353.
• [22]Park B, Kim W, Ruppert D, Jones M, Signorini D, Kohn R: Simple transformation techniques for improved non-parametric regression. Scand J Stat 1997, 24(2):145-163.
• [23]Efron B, Tibshirani R: An introduction to the bootstrap. New York: Chapman & Hall; 1993.
• [24]Olives C, Valadez J, Brooker S, Pagano M: Multiple category-lot quality assurance sampling: a new classification system with application to schistosomiasis control. PLoS Negl Trop Dis 2012, 6(9):e1806.
• [25]Stroh G, Birmingham M: Protocol for assessing neonatal tetanus mortality in the community using a combination of cluster and lot quality assurance sampling. Document WHO/V&B/02.05. Geneva: World Health Organization, Department of Vaccines and Biologicals 2002. Available from: http://whqlibdoc.who.int/hq/2002/WHO_V&B_02.05.pdf webcite. (last accessed 2/20/14)
• [26]Valadez JJ, Transgrud R, Mbugua M, Smith T: Assessing family planning service-delivery skills in Kenya. Stud Fam Plann 1997, 28(2):143-150.
• [27]Pezzoli L, Pineda S, Halkyer P, Crespo G, Andrews N, Ronveaux O: Cluster-sample surveys and lot quality assurance sampling to evaluate yellow fever immunisation coverage following a national campaign, Bolivia, 2007. Trop Med Int Health 2009, 14(3):355-361.
• [28]Hedt-Gauthier BL, Mitsunaga T, Hund L, Olives C, Pagano M: The effect of clustering on lot quality assurance sampling: a probabilistic model to calculate sample sizes for quality assessments. Emerging Themes Epidemiol 2013, 10(1):1-11. BioMed Central Full Text